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b/backbone_pure_tensors/minimal_test/save_minimal_tensor.py @@ -0,0 +1,10 @@ +import torch +from pathlib import Path + +out_dir = Path('.') +out_dir.mkdir(parents=True, exist_ok=True) + +# Save a simple tensor +x = torch.arange(12).reshape(3, 4).float() +torch.save(x, out_dir / 'test_tensor.pt') +print('Saved test_tensor.pt:', x) \ No newline at end of file diff --git a/build/CMakeFiles/3.22.1/CMakeDetermineCompilerABI_CUDA.bin b/build/CMakeFiles/3.22.1/CMakeDetermineCompilerABI_CUDA.bin index 9ab9f9c..18a4301 100644 Binary files a/build/CMakeFiles/3.22.1/CMakeDetermineCompilerABI_CUDA.bin and b/build/CMakeFiles/3.22.1/CMakeDetermineCompilerABI_CUDA.bin differ diff --git a/build/CMakeFiles/CMakeOutput.log b/build/CMakeFiles/CMakeOutput.log index 893470c..c9bda19 100644 --- a/build/CMakeFiles/CMakeOutput.log +++ b/build/CMakeFiles/CMakeOutput.log @@ -35,8 +35,8 @@ The output was: #$ _TARGET_DIR_=targets/x86_64-linux #$ TOP=/usr/local/cuda-11.8/bin/.. #$ NVVMIR_LIBRARY_DIR=/usr/local/cuda-11.8/bin/../nvvm/libdevice -#$ LD_LIBRARY_PATH=/usr/local/cuda-11.8/bin/../lib:/usr/local/cuda-11.8/lib64: -#$ PATH=/usr/local/cuda-11.8/bin/../nvvm/bin:/usr/local/cuda-11.8/bin:/usr/local/cuda-11.8/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/snap/bin:/home/mht/.local/share/JetBrains/Toolbox/scripts +#$ LD_LIBRARY_PATH=/usr/local/cuda-11.8/bin/../lib:/usr/local/cuda-11.8/lib64:/tmp/.mount_CursorV5YAoS/usr/lib/:/tmp/.mount_CursorV5YAoS/usr/lib32/:/tmp/.mount_CursorV5YAoS/usr/lib64/:/tmp/.mount_CursorV5YAoS/lib/:/tmp/.mount_CursorV5YAoS/lib/i386-linux-gnu/:/tmp/.mount_CursorV5YAoS/lib/x86_64-linux-gnu/:/tmp/.mount_CursorV5YAoS/lib/aarch64-linux-gnu/:/tmp/.mount_CursorV5YAoS/lib32/:/tmp/.mount_CursorV5YAoS/lib64/: +#$ PATH=/usr/local/cuda-11.8/bin/../nvvm/bin:/usr/local/cuda-11.8/bin:/usr/local/cuda-11.8/bin:/home/mht/anaconda3/bin:/home/mht/anaconda3/condabin:/tmp/.mount_CursorV5YAoS/usr/bin:/tmp/.mount_CursorV5YAoS/usr/sbin:/tmp/.mount_CursorV5YAoS/usr/games:/tmp/.mount_CursorV5YAoS/bin:/tmp/.mount_CursorV5YAoS/sbin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/snap/bin:/home/mht/.local/share/JetBrains/Toolbox/scripts:/home/mht/.local/share/JetBrains/Toolbox/scripts:/home/mht/.local/share/JetBrains/Toolbox/scripts #$ INCLUDES="-I/usr/local/cuda-11.8/bin/../targets/x86_64-linux/include" #$ LIBRARIES= "-L/usr/local/cuda-11.8/bin/../targets/x86_64-linux/lib/stubs" "-L/usr/local/cuda-11.8/bin/../targets/x86_64-linux/lib" #$ CUDAFE_FLAGS= @@ -60,7 +60,7 @@ Compilation of the CUDA compiler identification source "CMakeCUDACompilerId.cu" The CUDA compiler identification is NVIDIA, found in "/media/mht/ADATA/repos/cpp_tracker/build/CMakeFiles/3.22.1/CompilerIdCUDA/a.out" Parsed CUDA nvcc implicit link information from above output: - found 'PATH=' string: [/usr/local/cuda-11.8/bin/../nvvm/bin:/usr/local/cuda-11.8/bin:/usr/local/cuda-11.8/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/snap/bin:/home/mht/.local/share/JetBrains/Toolbox/scripts] + found 'PATH=' string: [/usr/local/cuda-11.8/bin/../nvvm/bin:/usr/local/cuda-11.8/bin:/usr/local/cuda-11.8/bin:/home/mht/anaconda3/bin:/home/mht/anaconda3/condabin:/tmp/.mount_CursorV5YAoS/usr/bin:/tmp/.mount_CursorV5YAoS/usr/sbin:/tmp/.mount_CursorV5YAoS/usr/games:/tmp/.mount_CursorV5YAoS/bin:/tmp/.mount_CursorV5YAoS/sbin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/snap/bin:/home/mht/.local/share/JetBrains/Toolbox/scripts:/home/mht/.local/share/JetBrains/Toolbox/scripts:/home/mht/.local/share/JetBrains/Toolbox/scripts] found 'LIBRARIES=' string: ["-L/usr/local/cuda-11.8/bin/../targets/x86_64-linux/lib/stubs" "-L/usr/local/cuda-11.8/bin/../targets/x86_64-linux/lib"] considering line: [#$ rm tmp/a_dlink.reg.c] considering line: [gcc -D__CUDA_ARCH__=520 -D__CUDA_ARCH_LIST__=520 -E -x c++ -DCUDA_DOUBLE_MATH_FUNCTIONS -D__CUDACC__ -D__NVCC__ "-I/usr/local/cuda-11.8/bin/../targets/x86_64-linux/include" -D__CUDACC_VER_MAJOR__=11 -D__CUDACC_VER_MINOR__=8 -D__CUDACC_VER_BUILD__=89 -D__CUDA_API_VER_MAJOR__=11 -D__CUDA_API_VER_MINOR__=8 -D__NVCC_DIAG_PRAGMA_SUPPORT__=1 -include "cuda_runtime.h" -m64 "CMakeCUDACompilerId.cu" -o "tmp/CMakeCUDACompilerId.cpp1.ii" ] @@ -108,7 +108,7 @@ Parsed CUDA nvcc implicit link information from above output: Parsed CUDA nvcc include information from above output: - found 'PATH=' string: [/usr/local/cuda-11.8/bin/../nvvm/bin:/usr/local/cuda-11.8/bin:/usr/local/cuda-11.8/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/snap/bin:/home/mht/.local/share/JetBrains/Toolbox/scripts] + found 'PATH=' string: [/usr/local/cuda-11.8/bin/../nvvm/bin:/usr/local/cuda-11.8/bin:/usr/local/cuda-11.8/bin:/home/mht/anaconda3/bin:/home/mht/anaconda3/condabin:/tmp/.mount_CursorV5YAoS/usr/bin:/tmp/.mount_CursorV5YAoS/usr/sbin:/tmp/.mount_CursorV5YAoS/usr/games:/tmp/.mount_CursorV5YAoS/bin:/tmp/.mount_CursorV5YAoS/sbin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/snap/bin:/home/mht/.local/share/JetBrains/Toolbox/scripts:/home/mht/.local/share/JetBrains/Toolbox/scripts:/home/mht/.local/share/JetBrains/Toolbox/scripts] found 'LIBRARIES=' string: ["-L/usr/local/cuda-11.8/bin/../targets/x86_64-linux/lib/stubs" "-L/usr/local/cuda-11.8/bin/../targets/x86_64-linux/lib"] considering line: [#$ rm tmp/a_dlink.reg.c] considering line: [gcc -D__CUDA_ARCH__=520 -D__CUDA_ARCH_LIST__=520 -E -x c++ -DCUDA_DOUBLE_MATH_FUNCTIONS -D__CUDACC__ -D__NVCC__ "-I/usr/local/cuda-11.8/bin/../targets/x86_64-linux/include" -D__CUDACC_VER_MAJOR__=11 -D__CUDACC_VER_MINOR__=8 -D__CUDACC_VER_BUILD__=89 -D__CUDA_API_VER_MAJOR__=11 -D__CUDA_API_VER_MINOR__=8 -D__NVCC_DIAG_PRAGMA_SUPPORT__=1 -include "cuda_runtime.h" -m64 "CMakeCUDACompilerId.cu" -o "tmp/CMakeCUDACompilerId.cpp1.ii" ] @@ -159,10 +159,10 @@ Parsed CUDA nvcc include information from above output: Detecting CXX compiler ABI info compiled with the following output: Change Dir: /media/mht/ADATA/repos/cpp_tracker/build/CMakeFiles/CMakeTmp -Run Build Command(s):/usr/bin/gmake -f Makefile cmTC_8534d/fast && /usr/bin/gmake -f CMakeFiles/cmTC_8534d.dir/build.make CMakeFiles/cmTC_8534d.dir/build +Run Build Command(s):/usr/bin/gmake -f Makefile cmTC_3a56d/fast && /usr/bin/gmake -f CMakeFiles/cmTC_3a56d.dir/build.make CMakeFiles/cmTC_3a56d.dir/build gmake[1]: Entering directory '/media/mht/ADATA/repos/cpp_tracker/build/CMakeFiles/CMakeTmp' -Building CXX object CMakeFiles/cmTC_8534d.dir/CMakeCXXCompilerABI.cpp.o -/usr/bin/c++ -v -o CMakeFiles/cmTC_8534d.dir/CMakeCXXCompilerABI.cpp.o -c /usr/share/cmake-3.22/Modules/CMakeCXXCompilerABI.cpp +Building CXX object CMakeFiles/cmTC_3a56d.dir/CMakeCXXCompilerABI.cpp.o +/usr/bin/c++ -v -o CMakeFiles/cmTC_3a56d.dir/CMakeCXXCompilerABI.cpp.o -c /usr/share/cmake-3.22/Modules/CMakeCXXCompilerABI.cpp Using built-in specs. COLLECT_GCC=/usr/bin/c++ OFFLOAD_TARGET_NAMES=nvptx-none:amdgcn-amdhsa @@ -172,8 +172,8 @@ Configured with: ../src/configure -v --with-pkgversion='Ubuntu 11.4.0-1ubuntu1~2 Thread model: posix Supported LTO compression algorithms: zlib zstd gcc version 11.4.0 (Ubuntu 11.4.0-1ubuntu1~22.04) -COLLECT_GCC_OPTIONS='-v' '-o' 'CMakeFiles/cmTC_8534d.dir/CMakeCXXCompilerABI.cpp.o' '-c' '-shared-libgcc' '-mtune=generic' '-march=x86-64' '-dumpdir' 'CMakeFiles/cmTC_8534d.dir/' - /usr/lib/gcc/x86_64-linux-gnu/11/cc1plus -quiet -v -imultiarch x86_64-linux-gnu -D_GNU_SOURCE /usr/share/cmake-3.22/Modules/CMakeCXXCompilerABI.cpp -quiet -dumpdir CMakeFiles/cmTC_8534d.dir/ -dumpbase CMakeCXXCompilerABI.cpp.cpp -dumpbase-ext .cpp -mtune=generic -march=x86-64 -version -fasynchronous-unwind-tables -fstack-protector-strong -Wformat -Wformat-security -fstack-clash-protection -fcf-protection -o /tmp/ccgJfSiR.s +COLLECT_GCC_OPTIONS='-v' '-o' 'CMakeFiles/cmTC_3a56d.dir/CMakeCXXCompilerABI.cpp.o' '-c' '-shared-libgcc' '-mtune=generic' '-march=x86-64' '-dumpdir' 'CMakeFiles/cmTC_3a56d.dir/' + /usr/lib/gcc/x86_64-linux-gnu/11/cc1plus -quiet -v -imultiarch x86_64-linux-gnu -D_GNU_SOURCE /usr/share/cmake-3.22/Modules/CMakeCXXCompilerABI.cpp -quiet -dumpdir CMakeFiles/cmTC_3a56d.dir/ -dumpbase CMakeCXXCompilerABI.cpp.cpp -dumpbase-ext .cpp -mtune=generic -march=x86-64 -version -fasynchronous-unwind-tables -fstack-protector-strong -Wformat -Wformat-security -fstack-clash-protection -fcf-protection -o /tmp/ccpTftdk.s GNU C++17 (Ubuntu 11.4.0-1ubuntu1~22.04) version 11.4.0 (x86_64-linux-gnu) compiled by GNU C version 11.4.0, GMP version 6.2.1, MPFR version 4.1.0, MPC version 1.2.1, isl version isl-0.24-GMP @@ -197,15 +197,15 @@ GNU C++17 (Ubuntu 11.4.0-1ubuntu1~22.04) version 11.4.0 (x86_64-linux-gnu) GGC heuristics: --param ggc-min-expand=100 --param ggc-min-heapsize=131072 Compiler executable checksum: d591828bb4d392ae8b7b160e5bb0b95f -COLLECT_GCC_OPTIONS='-v' '-o' 'CMakeFiles/cmTC_8534d.dir/CMakeCXXCompilerABI.cpp.o' '-c' '-shared-libgcc' '-mtune=generic' '-march=x86-64' '-dumpdir' 'CMakeFiles/cmTC_8534d.dir/' - as -v --64 -o CMakeFiles/cmTC_8534d.dir/CMakeCXXCompilerABI.cpp.o /tmp/ccgJfSiR.s +COLLECT_GCC_OPTIONS='-v' '-o' 'CMakeFiles/cmTC_3a56d.dir/CMakeCXXCompilerABI.cpp.o' '-c' '-shared-libgcc' '-mtune=generic' '-march=x86-64' '-dumpdir' 'CMakeFiles/cmTC_3a56d.dir/' + as -v --64 -o CMakeFiles/cmTC_3a56d.dir/CMakeCXXCompilerABI.cpp.o /tmp/ccpTftdk.s GNU assembler version 2.38 (x86_64-linux-gnu) using BFD version (GNU Binutils for Ubuntu) 2.38 COMPILER_PATH=/usr/lib/gcc/x86_64-linux-gnu/11/:/usr/lib/gcc/x86_64-linux-gnu/11/:/usr/lib/gcc/x86_64-linux-gnu/:/usr/lib/gcc/x86_64-linux-gnu/11/:/usr/lib/gcc/x86_64-linux-gnu/ LIBRARY_PATH=/usr/lib/gcc/x86_64-linux-gnu/11/:/usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu/:/usr/lib/gcc/x86_64-linux-gnu/11/../../../../lib/:/lib/x86_64-linux-gnu/:/lib/../lib/:/usr/lib/x86_64-linux-gnu/:/usr/lib/../lib/:/usr/lib/gcc/x86_64-linux-gnu/11/../../../:/lib/:/usr/lib/ -COLLECT_GCC_OPTIONS='-v' '-o' 'CMakeFiles/cmTC_8534d.dir/CMakeCXXCompilerABI.cpp.o' '-c' '-shared-libgcc' '-mtune=generic' '-march=x86-64' '-dumpdir' 'CMakeFiles/cmTC_8534d.dir/CMakeCXXCompilerABI.cpp.' -Linking CXX executable cmTC_8534d -/usr/bin/cmake -E cmake_link_script CMakeFiles/cmTC_8534d.dir/link.txt --verbose=1 -/usr/bin/c++ -v CMakeFiles/cmTC_8534d.dir/CMakeCXXCompilerABI.cpp.o -o cmTC_8534d +COLLECT_GCC_OPTIONS='-v' '-o' 'CMakeFiles/cmTC_3a56d.dir/CMakeCXXCompilerABI.cpp.o' '-c' '-shared-libgcc' '-mtune=generic' '-march=x86-64' '-dumpdir' 'CMakeFiles/cmTC_3a56d.dir/CMakeCXXCompilerABI.cpp.' +Linking CXX executable cmTC_3a56d +/usr/bin/cmake -E cmake_link_script CMakeFiles/cmTC_3a56d.dir/link.txt --verbose=1 +/usr/bin/c++ -v CMakeFiles/cmTC_3a56d.dir/CMakeCXXCompilerABI.cpp.o -o cmTC_3a56d Using built-in specs. 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ignore line: [Building CXX object CMakeFiles/cmTC_8534d.dir/CMakeCXXCompilerABI.cpp.o] - ignore line: [/usr/bin/c++ -v -o CMakeFiles/cmTC_8534d.dir/CMakeCXXCompilerABI.cpp.o -c /usr/share/cmake-3.22/Modules/CMakeCXXCompilerABI.cpp] + ignore line: [Building CXX object CMakeFiles/cmTC_3a56d.dir/CMakeCXXCompilerABI.cpp.o] + ignore line: [/usr/bin/c++ -v -o CMakeFiles/cmTC_3a56d.dir/CMakeCXXCompilerABI.cpp.o -c /usr/share/cmake-3.22/Modules/CMakeCXXCompilerABI.cpp] ignore line: [Using built-in specs.] ignore line: [COLLECT_GCC=/usr/bin/c++] ignore line: [OFFLOAD_TARGET_NAMES=nvptx-none:amdgcn-amdhsa] @@ -263,8 +263,8 @@ Parsed CXX implicit link information from above output: ignore line: [Thread model: posix] ignore line: [Supported LTO compression algorithms: zlib zstd] ignore line: [gcc version 11.4.0 (Ubuntu 11.4.0-1ubuntu1~22.04) ] - ignore line: [COLLECT_GCC_OPTIONS='-v' '-o' 'CMakeFiles/cmTC_8534d.dir/CMakeCXXCompilerABI.cpp.o' '-c' '-shared-libgcc' '-mtune=generic' '-march=x86-64' '-dumpdir' 'CMakeFiles/cmTC_8534d.dir/'] - 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differ diff --git a/compare_preprocessed_inputs_debug.py b/compare_preprocessed_inputs_debug.py new file mode 100644 index 0000000..6d32a15 --- /dev/null +++ b/compare_preprocessed_inputs_debug.py @@ -0,0 +1,23 @@ +import torch + +def get_tensor(x): + if isinstance(x, torch.Tensor): + return x + if hasattr(x, 'parameters') and len(list(x.parameters())) > 0: + return list(x.parameters())[0] + if callable(x): + try: + out = x() + if isinstance(out, torch.Tensor): + return out + except Exception: + pass + raise RuntimeError('Could not extract tensor') + +a = torch.load('test/output/resnet/sample_0_image_preprocessed_python.pt', weights_only=False) +b = torch.load('test/output/resnet/sample_0_image_preprocessed_cpp.pt', weights_only=False) +a = get_tensor(a) +b = get_tensor(b) +print('py:', a.shape, a.dtype, a.min().item(), a.max().item(), a.mean().item()) +print('cpp:', b.shape, b.dtype, b.min().item(), b.max().item(), b.mean().item()) +print('diff:', (a-b).abs().max().item(), (a-b).abs().mean().item()) \ No newline at end of file diff --git a/compare_resnet_outputs_debug.py b/compare_resnet_outputs_debug.py new file mode 100644 index 0000000..fb598ca --- /dev/null +++ b/compare_resnet_outputs_debug.py @@ -0,0 +1,42 @@ +import torch + +def get_tensor(x): + if isinstance(x, torch.Tensor): + return x + if hasattr(x, 'parameters') and len(list(x.parameters())) > 0: + return list(x.parameters())[0] + if callable(x): + try: + out = x() + if isinstance(out, torch.Tensor): + return out + except Exception: + pass + raise RuntimeError('Could not extract tensor') + +def compare_tensors(py_path, cpp_path, label): + a = torch.load(py_path) + b = torch.load(cpp_path, weights_only=False) + b = get_tensor(b) + print(f'--- {label} ---') + print('py:', a.shape, a.dtype, a.min().item(), a.max().item(), a.mean().item()) + print('cpp:', b.shape, b.dtype, b.min().item(), b.max().item(), b.mean().item()) + print('diff:', (a-b).abs().max().item(), (a-b).abs().mean().item()) + +compare_tensors('test/output_py/resnet_py/sample_0/conv1_output.pt', 'test/output/resnet/sample_0_conv1_output.pt', 'conv1_output') +compare_tensors('test/output_py/resnet_py/sample_0/debug_resnet_conv1_output_for_bn1_input.pt', 'test/output/resnet/sample_0_debug_resnet_conv1_output_for_bn1_input.pt', 'debug_resnet_conv1_output_for_bn1_input') +compare_tensors('test/output_py/resnet_py/sample_0/bn1_output.pt', 'test/output/resnet/sample_0_bn1_output.pt', 'bn1_output') + +# Print BN1 epsilon and momentum from Python model +try: + from ltr.models.backbone import resnet50 + model = resnet50(output_layers=['layer1', 'layer2', 'layer3', 'layer4'], pretrained=False) + model.load_state_dict(torch.load('backbone_pure_tensors/state_dict.pt')) + print('\nPython BN1 epsilon:', model.bn1.eps) + print('Python BN1 momentum:', model.bn1.momentum) +except Exception as e: + print('Could not print Python BN1 eps/momentum:', e) + +# Print expected C++ BN1 epsilon and momentum (from code) +print('C++ BN1 epsilon: 1e-5 (from C++ code)') +print('C++ BN1 momentum: 0.1 (from C++ code)') \ No newline at end of file diff --git a/export_and_verify_backbone_tensors.py b/export_and_verify_backbone_tensors.py new file mode 100644 index 0000000..2a3683f --- /dev/null +++ b/export_and_verify_backbone_tensors.py @@ -0,0 +1,19 @@ +import os +import torch +from pathlib import Path + +src_dir = Path('exported_weights/backbone') +dst_dir = Path('exported_weights/backbone_verified') +dst_dir.mkdir(parents=True, exist_ok=True) + +for file in src_dir.glob('*.pt'): + try: + obj = torch.load(file, map_location='cpu') + if not isinstance(obj, torch.Tensor): + print(f"[WARNING] {file.name} is not a tensor (type: {type(obj)}), skipping.") + continue + out_path = dst_dir / file.name + torch.save(obj, out_path) + print(f"[OK] {file.name} loaded and re-saved to {out_path}") + except Exception as e: + print(f"[ERROR] Failed to load {file.name}: {e}") \ No newline at end of file diff --git a/export_backbone_from_python_tracker.py b/export_backbone_from_python_tracker.py new file mode 100644 index 0000000..799ed90 --- /dev/null +++ b/export_backbone_from_python_tracker.py @@ -0,0 +1,28 @@ +import os +import torch +from pathlib import Path +from ltr.models.backbone import resnet50 + +# Directory to save pure tensor weights +out_dir = Path('exported_weights/backbone_pure_tensors') +out_dir.mkdir(parents=True, exist_ok=True) + +# Load backbone as in the tracker +model = resnet50(output_layers=['layer1', 'layer2', 'layer3', 'layer4'], pretrained=False) + +# Load weights from the split files (original directory) +def load_weights_from_tensors(model, tensor_dir): + sd = model.state_dict() + for k in sd: + tensor_path = Path(tensor_dir) / (k.replace('.', '_') + '.pt') + if tensor_path.exists(): + sd[k] = torch.load(tensor_path, map_location='cpu') + model.load_state_dict(sd) + +load_weights_from_tensors(model, 'exported_weights/backbone') + +# Save each parameter as a pure tensor +for name, param in model.state_dict().items(): + out_path = out_dir / (name.replace('.', '_') + '.pt') + torch.save(param.detach().cpu(), out_path) + print(f"[OK] Saved {out_path}") \ No newline at end of file diff --git a/exported_weights/backbone_pure_tensors/bn1_bias.pt b/exported_weights/backbone_pure_tensors/bn1_bias.pt new file mode 100644 index 0000000..d3922a3 Binary files /dev/null and b/exported_weights/backbone_pure_tensors/bn1_bias.pt differ diff --git a/exported_weights/backbone_pure_tensors/bn1_num_batches_tracked.pt b/exported_weights/backbone_pure_tensors/bn1_num_batches_tracked.pt new file mode 100644 index 0000000..e84f681 Binary files /dev/null and b/exported_weights/backbone_pure_tensors/bn1_num_batches_tracked.pt differ diff --git a/exported_weights/backbone_pure_tensors/bn1_running_mean.pt b/exported_weights/backbone_pure_tensors/bn1_running_mean.pt new file mode 100644 index 0000000..aa905ba Binary files /dev/null and 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@@ -0,0 +1,74 @@ +#!/usr/bin/env python3 +""" +Compare the inputs to the BB regressor (layer2, layer3, proposals, bbox) between C++ and Python for sample 0. +Print shape, min, max, mean, and check for equality. +""" +import torch +from pathlib import Path + +def extract_tensor_from_jit(jit_module): + try: + params = list(jit_module.parameters()) + if params: + return params[0] + buffers = list(jit_module.buffers()) + if buffers: + return buffers[0] + except Exception: + pass + return None + +def print_stats(label, t1, t2): + print(f"{label}:") + print(f" C++: shape={t1.shape}, min={t1.min().item():.6f}, max={t1.max().item():.6f}, mean={t1.mean().item():.6f}") + print(f" Py: shape={t2.shape}, min={t2.min().item():.6f}, max={t2.max().item():.6f}, mean={t2.mean().item():.6f}") + if t1.shape == t2.shape: + same = torch.allclose(t1, t2, atol=1e-6) + print(f" Allclose: {same}") + print(f" Max abs diff: {(t1-t2).abs().max().item():.6f}") + else: + print(" Shapes differ!") + print() + +def main(): + # Directories + cpp_out = Path("test/output") + py_in = Path("test/input_samples/common") + + # Sample index + idx = 0 + + # ResNet features (layer2, layer3) + cpp_layer2 = extract_tensor_from_jit(torch.jit.load(str(cpp_out / "resnet" / f"sample_{idx}_layer2.pt"))) + cpp_layer3 = extract_tensor_from_jit(torch.jit.load(str(cpp_out / "resnet" / f"sample_{idx}_layer3.pt"))) + py_layer2 = extract_tensor_from_jit(torch.jit.load(str(py_in / f"sample_{idx}_layer2.pt"))) if (py_in / f"sample_{idx}_layer2.pt").exists() else None + py_layer3 = extract_tensor_from_jit(torch.jit.load(str(py_in / f"sample_{idx}_layer3.pt"))) if (py_in / f"sample_{idx}_layer3.pt").exists() else None + + # Proposals + cpp_proposals = extract_tensor_from_jit(torch.jit.load(str(cpp_out / "bb_regressor" / f"sample_{idx}_proposals.pt"))) if (cpp_out / "bb_regressor" / f"sample_{idx}_proposals.pt").exists() else None + py_proposals = extract_tensor_from_jit(torch.jit.load(str(py_in / f"sample_{idx}_proposals.pt"))) if (py_in / f"sample_{idx}_proposals.pt").exists() else None + + # BBox + cpp_bb = extract_tensor_from_jit(torch.jit.load(str(cpp_out / "bb_regressor" / f"sample_{idx}_bb.pt"))) if (cpp_out / "bb_regressor" / f"sample_{idx}_bb.pt").exists() else None + py_bb = extract_tensor_from_jit(torch.jit.load(str(py_in / f"sample_{idx}_bb.pt"))) if (py_in / f"sample_{idx}_bb.pt").exists() else None + + print("=== BB Regressor Input Comparison (Sample 0) ===\n") + if cpp_layer2 is not None and py_layer2 is not None: + print_stats("Layer2", cpp_layer2, py_layer2) + else: + print("Layer2: Could not load both tensors\n") + if cpp_layer3 is not None and py_layer3 is not None: + print_stats("Layer3", cpp_layer3, py_layer3) + else: + print("Layer3: Could not load both tensors\n") + if cpp_proposals is not None and py_proposals is not None: + print_stats("Proposals", cpp_proposals, py_proposals) + else: + print("Proposals: Could not load both tensors\n") + if cpp_bb is not None and py_bb is not None: + print_stats("BBox", cpp_bb, py_bb) + else: + print("BBox: Could not load both tensors\n") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/test/compare_models.py b/test/compare_models.py index c2e01e1..edcd3a4 100644 --- a/test/compare_models.py +++ b/test/compare_models.py @@ -675,6 +675,19 @@ class ComparisonRunner: if current_errors: self.all_comparison_stats[f"BBReg_Sample_{i}"] = current_errors + # For sample 0, save these tensors for comparison + if i == 0: + out_dir = Path('test/input_samples/common') + out_dir.mkdir(parents=True, exist_ok=True) + if py_feat_layer2 is not None: + save_tensor_jit(py_feat_layer2.cpu(), out_dir / f'sample_0_layer2.pt') + if py_feat_layer3 is not None: + save_tensor_jit(py_feat_layer3.cpu(), out_dir / f'sample_0_layer3.pt') + if py_proposals is not None: + save_tensor_jit(py_proposals.cpu(), out_dir / f'sample_0_proposals.pt') + if py_init_bbox is not None: + save_tensor_jit(py_init_bbox.cpu(), out_dir / f'sample_0_bb.pt') + def compare_resnet_outputs(self): print("\\n--- Comparing ResNet Outputs ---") if not self.models.get('ResNet'): @@ -1714,6 +1727,17 @@ class ComparisonRunner: i += 1 model.eval().to(device) +# Add this function near the top-level utilities + +def save_tensor_jit(tensor, path): + """Save a tensor as a JIT-wrapped module for compatibility with C++ outputs.""" + class TensorContainer(torch.nn.Module): + def __init__(self, t): + super().__init__() + self.tensor = torch.nn.Parameter(t) + tc = TensorContainer(tensor) + scripted = torch.jit.script(tc) + torch.jit.save(scripted, str(path)) if __name__ == "__main__": # Define ROOT_DIR for standalone script execution diff --git a/test/inspect_bn1_cpp_tensor.py b/test/inspect_bn1_cpp_tensor.py new file mode 100644 index 0000000..6a603f9 --- /dev/null +++ b/test/inspect_bn1_cpp_tensor.py @@ -0,0 +1,23 @@ +import torch + +# Inspect BN1 output +print('--- BN1 Output ---') +m = torch.jit.load('test/output/resnet/sample_0_bn1_output.pt', map_location='cpu') +for name, p in m.named_parameters(): + print(f'{name}: shape={p.shape}, dtype={p.dtype}, device={p.device}, first5={p.flatten()[:5]}') +for name, b in m.named_buffers(): + print(f'{name}: shape={b.shape}, dtype={b.dtype}, device={b.device}, first5={b.flatten()[:5]}') + +# Inspect BN1 input +print('\n--- BN1 Input (Conv1 Output for BN1 Input) ---') +m_in = torch.jit.load('test/output/resnet/sample_0_debug_resnet_conv1_output_for_bn1_input.pt', map_location='cpu') +for name, p in m_in.named_parameters(): + print(f'{name}: shape={p.shape}, dtype={p.dtype}, device={p.device}, first5={p.flatten()[:5]}') +for name, b in m_in.named_buffers(): + print(f'{name}: shape={b.shape}, dtype={b.dtype}, device={b.device}, first5={b.flatten()[:5]}') + +# Inspect BN1 parameters and stats from exported weights +print('\n--- BN1 Parameters and Stats from Exported Weights ---') +for param in ['weight', 'bias', 'running_mean', 'running_var']: + t = torch.load(f'exported_weights/backbone/bn1_{param}.pt', map_location='cpu') + print(f'bn1_{param}: shape={t.shape}, dtype={t.dtype}, device={t.device}, first5={t.flatten()[:5]}') \ No newline at end of file diff --git a/test/inspect_bn1_python_tensor.py b/test/inspect_bn1_python_tensor.py new file mode 100644 index 0000000..d867b8e --- /dev/null +++ b/test/inspect_bn1_python_tensor.py @@ -0,0 +1,15 @@ +import sys +import os +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +import torch +from ltr.models.backbone.resnet import resnet50 + +# Instantiate the model +model = resnet50() +model.eval() + +# Print BN1 parameters and stats +bn1 = model.bn1 +for param in ['weight', 'bias', 'running_mean', 'running_var']: + t = getattr(bn1, param) + print(f'bn1_{param}: shape={t.shape}, dtype={t.dtype}, device={t.device}, first5={t.flatten()[:5]}') \ No newline at end of file diff --git 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a/test/output_py/resnet_py/sample_2/layer4.pt b/test/output_py/resnet_py/sample_2/layer4.pt new file mode 100644 index 0000000..7389aba Binary files /dev/null and b/test/output_py/resnet_py/sample_2/layer4.pt differ diff --git a/test/test_models.cpp b/test/test_models.cpp index 28c69d5..d6d100f 100644 --- a/test/test_models.cpp +++ b/test/test_models.cpp @@ -34,8 +34,33 @@ void ensure_dir_exists(const std::string& path_str) { torch::Tensor load_tensor_from_file(const std::string& file_path, torch::Device device) { std::cout << " Loading tensor from: " << file_path << std::endl; torch::Tensor tensor; - torch::load(tensor, file_path); - return tensor.to(device); + try { + torch::load(tensor, file_path); + return tensor.to(device); + } catch (const c10::Error& e) { + std::cerr << " Direct torch::load failed, trying as JIT module: " << e.what() << std::endl; + try { + auto module = torch::jit::load(file_path, device); + // Try to extract parameter named 'tensor' (as in TensorContainer) + for (const auto& p : module.named_parameters()) { + if (p.name == "tensor") { + std::cout << " Extracted 'tensor' parameter from JIT module." << std::endl; + return p.value.to(device); + } + } + // Try buffers if not found in parameters + for (const auto& b : module.named_buffers()) { + if (b.name == "tensor") { + std::cout << " Extracted 'tensor' buffer from JIT module." << std::endl; + return b.value.to(device); + } + } + throw std::runtime_error("No 'tensor' parameter or buffer found in JIT module: " + file_path); + } catch (const c10::Error& e2) { + std::cerr << " Failed to load as JIT module: " << e2.what() << std::endl; + throw; + } + } } // Helper to save a tensor @@ -65,6 +90,27 @@ torch::Tensor get_or_generate_tensor( } } +// Helper to save a tensor as a JIT-wrapped module (matching Python TensorContainer) +void save_tensor_as_jit_module(const torch::Tensor& tensor, const std::string& file_path) { + // Define a simple module with a single parameter + struct TensorContainerImpl : torch::nn::Module { + TensorContainerImpl(const torch::Tensor& t) { + tensor = register_parameter("tensor", t.clone()); + } + torch::Tensor tensor; + }; + // Create and script the module + auto module = std::make_shared(tensor); + auto scripted = torch::jit::script::Module("TensorContainer"); + scripted.register_parameter("tensor", module->tensor, /*is_buffer=*/false); + // Save as JIT module + try { + scripted.save(file_path); + } catch (const c10::Error& e) { + std::cerr << "Error saving JIT-wrapped tensor to " << file_path << ": " << e.what() << std::endl; + } +} + int main(int argc, char* argv[]) { if (argc < 4) { // Original usage message for consistency with how run_tests.sh calls it @@ -114,9 +160,9 @@ int main(int argc, char* argv[]) { // --- Initialize Models (once) --- std::cout << "--- Initializing Models ---" << std::endl; - std::string resnet_weights_path = "../exported_weights/backbone_regenerated"; - std::string bb_reg_weights_path = "../exported_weights/bb_regressor"; - std::string classifier_weights_path = "../exported_weights/classifier"; + std::string resnet_weights_path = "exported_weights/backbone_regenerated"; + std::string bb_reg_weights_path = "exported_weights/bb_regressor"; + std::string classifier_weights_path = "exported_weights/classifier"; std::optional resnet_model_opt; // Use TORCH_MODULE wrapper types directly std::optional classifier_model_opt_wrapped; @@ -229,6 +275,20 @@ int main(int argc, char* argv[]) { save_tensor_to_file(image_tensor, cpp_preprocessed_save_path.string()); std::cout << "Saved C++ preprocessed image for sample " << i << " to " << cpp_preprocessed_save_path.string() << std::endl; + // Save proposals and bbox as JIT-wrapped modules for sample 0 + if (i == 0) { + if (fs::exists(proposals_file)) { + auto proposals_tensor = load_tensor_from_file(proposals_file, device); + save_tensor_as_jit_module(proposals_tensor, (common_input_path / (sample_suffix + "_proposals.pt")).string()); + std::cout << "Saved proposals as JIT-wrapped module for sample 0." << std::endl; + } + if (fs::exists(bb_file)) { + auto bb_tensor = load_tensor_from_file(bb_file, device); + save_tensor_as_jit_module(bb_tensor, (common_input_path / (sample_suffix + "_bb.pt")).string()); + std::cout << "Saved bbox as JIT-wrapped module for sample 0." << std::endl; + } + } + // 1. 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